using Pkg
Pkg.activate(".")
Pkg.instantiate()
Pkg.update()
Pkg.status()

  Activating project at `~/Desktop/modal-symbolic-learning-course`
    Updating registry at `~/.julia/registries/General`
    Updating git-repo `https://github.com/JuliaRegistries/General.git`
   Installed BenchmarkTools ───── v1.4.0
   Installed LoweredCodeUtils ─── v2.3.2
   Installed ModalDecisionTrees ─ v0.3.3
   Installed SoleLogics ───────── v0.6.11
   Installed SoleModels ───────── v0.5.3
    Updating `~/Desktop/modal-symbolic-learning-course/Project.toml`
  [e54bda2e] ↑ ModalDecisionTrees v0.3.2 ⇒ v0.3.3
  [b002da8f] ↑ SoleLogics v0.6.10 ⇒ v0.6.11
  [4249d9c7] ↑ SoleModels v0.5.2 ⇒ v0.5.3
    Updating `~/Desktop/modal-symbolic-learning-course/Manifest.toml`
  [6e4b80f9] ↑ BenchmarkTools v1.3.2 ⇒ v1.4.0
  [6f1432cf] ↑ LoweredCodeUtils v2.3.1 ⇒ v2.3.2
  [e54bda2e] ↑ ModalDecisionTrees v0.3.2 ⇒ v0.3.3
  [b002da8f] ↑ SoleLogics v0.6.10 ⇒ v0.6.11
  [4249d9c7] ↑ SoleModels v0.5.2 ⇒ v0.5.3
Precompiling project...
  ✓ BenchmarkTools
  ✓ LoweredCodeUtils
  ✓ Revise
  ✓ SoleLogics
  ✓ SoleModels

# Import libraries for statistics & Machine Learning
using Random
using DataFrames
using Plots
using StatsBase

using MLJ
using Sole

using JLD2
using DataFrames # (Maybe need version v1.6.1?)
JLD2.@load "COVID-dataset.jld2"
(X1_df, X2_df, y) = dataset;

countmap(y)

Dict{String, Int64} with 2 entries:
  "POSITIVE" => 91
  "NEGATIVE" => 106

using SoleData

X_multimodal = MultiModalDataset([X1_df, X2_df])

● MultiModalDataset{DataFrame}
   └─ dimensionalities: (1, 1)
- Modality 1 / 2
   └─ dimensionality: 1
197×30 SubDataFrame
 Row │ cough_F1                           cough_F2                           c ⋯
     │ SubArray…                          SubArray…                          S ⋯
─────┼──────────────────────────────────────────────────────────────────────────
   1 │ [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0…  [0.0658202, 0.0389503, 0.505408,…  [ ⋯
   2 │ [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0…  [1.62005, 0.648436, 0.248455, 0.…  [
   3 │ [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0…  [2.95991, 1.13398, 0.151934, 0.1…  [
   4 │ [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0…  [3.66094, 0.709991, 0.342895, 0.…  [
   5 │ [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0…  [1.26265, 0.966647, 0.281845, 0.…  [ ⋯
   6 │ [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0…  [4.12442, 2.09424, 0.267123, 0.1…  [
   7 │ [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0…  [3.42468, 1.26102, 0.164075, 0.0…  [
   8 │ [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0…  [10.8754, 0.646552, 0.395368, 0.…  [
   9 │ [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0…  [18.1984, 2.61893, 1.77536, 1.02…  [ ⋯
  10 │ [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0…  [3.37013, 1.19436, 0.97079, 0.60…  [
  11 │ [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0…  [2.34154, 0.925767, 0.378433, 0.…  [
  ⋮  │                 ⋮                                  ⋮                    ⋱
 188 │ [0.0726378, 0.00766837, 0.016578…  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0…  [
 189 │ [0.35084, 0.093121, 0.0471824, 0…  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0…  [ ⋯
 190 │ [0.00220116, 0.0199147, 0.166516…  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0…  [
 191 │ [0.0770436, 0.00820453, 0.001445…  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0…  [
 192 │ [0.0453022, 0.00469916, 0.001939…  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0…  [
 193 │ [3.23477e-5, 0.000132317, 0.0001…  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0…  [ ⋯
 194 │ [0.127782, 0.101379, 0.0369538, …  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0…  [
 195 │ [0.00149723, 0.000397901, 0.0010…  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0…  [
 196 │ [0.00252365, 0.0540424, 0.065236…  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0…  [
 197 │ [0.115361, 0.0464483, 0.0123202,…  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0…  [ ⋯
                                                 28 columns and 176 rows omitted
- Modality 2 / 2
   └─ dimensionality: 1
197×30 SubDataFrame
 Row │ breath_F1                          breath_F2                          b ⋯
     │ Array…                             Array…                             A ⋯
─────┼──────────────────────────────────────────────────────────────────────────
   1 │ [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0…  [0.0275974, 0.123487, 0.954918, …  [ ⋯
   2 │ [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0…  [0.00551542, 0.738461, 1.23345, …  [
   3 │ [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0…  [0.0171132, 0.0128541, 0.175568,…  [
   4 │ [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0…  [0.0713483, 0.191903, 0.0599709,…  [
   5 │ [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0…  [0.0346951, 0.026089, 6.83611, 2…  [ ⋯
   6 │ [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0…  [0.0372815, 1.61098, 7.59596, 1.…  [
   7 │ [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0…  [0.0179035, 0.0158171, 6.72805, …  [
   8 │ [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0…  [0.038656, 0.202587, 0.0588298, …  [
   9 │ [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0…  [0.177666, 0.296219, 0.00581333,…  [ ⋯
  10 │ [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0…  [0.407654, 0.4168, 0.0640127, 0.…  [
  11 │ [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0…  [0.0442603, 0.0363921, 1.94756, …  [
  ⋮  │                 ⋮                                  ⋮                    ⋱
 188 │ [0.000519218, 0.000857345, 0.000…  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0…  [
 189 │ [0.0134826, 0.495593, 0.0, 0.0, …  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0…  [ ⋯
 190 │ [0.000243355, 0.000393873, 0.000…  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0…  [
 191 │ [0.000504018, 0.00280926, 0.0030…  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0…  [
 192 │ [0.00447856, 0.0114002, 0.004106…  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0…  [
 193 │ [0.00081567, 0.00117782, 0.00106…  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0…  [ ⋯
 194 │ [0.101095, 0.472197, 0.0360748, …  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0…  [
 195 │ [0.00159912, 0.00231749, 0.00020…  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0…  [
 196 │ [0.0280692, 0.0461481, 0.218395,…  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0…  [
 197 │ [0.251001, 0.0259159, 0.0174663,…  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0…  [ ⋯
                                                 28 columns and 176 rows omitted

modality(X_multimodal, 1) # Cough modality

names(X1_df)

30-element Vector{String}:
 "cough_F1"
 "cough_F2"
 "cough_F3"
 "cough_F4"
 "cough_F5"
 "cough_F6"
 "cough_F7"
 "cough_F8"
 "cough_F9"
 "cough_F10"
 "cough_F11"
 "cough_F12"
 "cough_F13"
 ⋮
 "cough_F19"
 "cough_F20"
 "cough_F21"
 "cough_F22"
 "cough_F23"
 "cough_F24"
 "cough_F25"
 "cough_F26"
 "cough_F27"
 "cough_F28"
 "cough_F29"
 "cough_F30"

# Let's inspect some instances
plot(map(i->plot(collect(X1_df[i,:]), labels=nothing,title=y[i]), 1:35:(35*6))..., layout = (2, 3), size = (1500,400))

# All instances, grouped by class
plot(map((((X_mod, mod_name), _y),)->plot(collect.(eachrow(X_mod[findall(__y->__y == _y, y),:])), labels=nothing,title="$(_y) $(mod_name)"), Iterators.product(zip(eachmodality(X_multimodal), ["cough", "breath"]), unique(y)))..., layout = (2, 2), size = (1500,400))

using ModalDecisionTrees

untrained Machine; caches model-specific representations of data
  model: ModalDecisionTree(max_depth = nothing, …)
  args: 
    1:	Source @567 ⏎ Table{AbstractVector{AbstractVector{Continuous}}}
    2:	Source @759 ⏎ AbstractVector{Textual}

# Train in cross-validation!
e = evaluate!(machine(ModalDecisionTree(; relations = :RCC8), X_multimodal, y);
        resampling=StratifiedCV(shuffle=true, nfolds = 3),
        measures=[accuracy],
        verbosity=0,
        check_measure=false
)

[ Info: Precomputing logiset...

# Accuracies per fold
e.per_fold

e.report_per_fold[2].solemodel

▣ {1}(⟨G⟩(min[V31] ≥ 0.001853897887096119))
├✔ {1}(⟨G⟩((min[V31] ≥ 0.001853897887096119) ∧ ⟨A̅O̅⟩(min[V27] < 76685.17720224746)))
│├✔ {1}(⟨G⟩((min[V31] ≥ 0.001853897887096119) ∧ ⟨A̅O̅⟩((min[V27] < 76685.17720224746) ∧ (min[V28] < 44762.952262639636))))
││├✔ {1}(⟨G⟩((min[V31] ≥ 0.001853897887096119) ∧ ⟨A̅O̅⟩((min[V27] < 76685.17720224746) ∧ (min[V28] < 44762.952262639636) ∧ ⟨D̅B̅E̅⟩(min[V50] ≥ 1885.5712367271199))))
│││├✔ {1}(⟨G⟩((min[V31] ≥ 0.001853897887096119) ∧ ⟨A̅O̅⟩((min[V27] < 76685.17720224746) ∧ (min[V28] < 44762.952262639636) ∧ ⟨D̅B̅E̅⟩((min[V50] ≥ 1885.5712367271199) ∧ (min[V13] ≥ 1.400957923736261)))))
││││├✔ NEGATIVE
││││└✘ POSITIVE
│││└✘ NEGATIVE
││└✘ POSITIVE
│└✘ NEGATIVE
└✘ {1}(⟨G⟩(min[V27] ≥ 2.170219509362793e6))
 ├✔ {1}(⟨G⟩((min[V27] ≥ 2.170219509362793e6) ∧ ⟨G⟩(min[V46] ≥ 5838.390775457752)))
 │├✔ POSITIVE
 │└✘ {1}(⟨G⟩((min[V27] ≥ 2.170219509362793e6) ∧ (min[V19] ≥ 30000.48907253628)))
 │ ├✔ NEGATIVE
 │ └✘ {1}(⟨G⟩((min[V27] ≥ 2.170219509362793e6) ∧ (min[V21] < 20414.774427686534)))
 │  ├✔ NEGATIVE
 │  └✘ POSITIVE
 └✘ {1}(⟨G⟩(min[V28] ≥ 553537.8712413169))
  ├✔ POSITIVE
  └✘ {1}(⟨G⟩(min[V2] ≥ 0.17083754207872534))
   ├✔ {1}(⟨G⟩((min[V2] ≥ 0.17083754207872534) ∧ (min[V11] ≥ 1108.1113127342035)))
   │├✔ NEGATIVE
   │└✘ POSITIVE
   └✘ NEGATIVE

report(mach).solemodel

▣ {1}(⟨G⟩(min[V37] ≥ 0.0013205573259328035))
├✔ {1}(⟨G⟩((min[V37] ≥ 0.0013205573259328035) ∧ ⟨L̅⟩(min[V27] ≥ 226321.9912113276)))
│├✔ NEGATIVE
│└✘ {1}(⟨G⟩((min[V37] ≥ 0.0013205573259328035) ∧ (min[V48] ≥ 1242.1904769961468)))
│ ├✔ {1}(⟨G⟩((min[V37] ≥ 0.0013205573259328035) ∧ (min[V48] ≥ 1242.1904769961468) ∧ ⟨AO⟩(min[V40] ≥ 3.51978641624239)))
│ │├✔ {1}(⟨G⟩((min[V37] ≥ 0.0013205573259328035) ∧ (min[V48] ≥ 1242.1904769961468) ∧ ⟨AO⟩((min[V40] ≥ 3.51978641624239) ∧ ⟨AO⟩(min[V31] ≥ 1.6207745800042226e-5))))
│ ││├✔ POSITIVE
│ ││└✘ NEGATIVE
│ │└✘ POSITIVE
│ └✘ NEGATIVE
└✘ {1}(⟨G⟩(min[V27] ≥ 2.170219509362793e6))
 ├✔ {1}(⟨G⟩((min[V27] ≥ 2.170219509362793e6) ∧ ⟨G⟩(min[V46] ≥ 5838.390775457752)))
 │├✔ POSITIVE
 │└✘ {1}(⟨G⟩((min[V27] ≥ 2.170219509362793e6) ∧ (min[V30] < 1.6492884918659376e6)))
 │ ├✔ POSITIVE
 │ └✘ NEGATIVE
 └✘ {1}(⟨G⟩(min[V28] ≥ 672777.1743629333))
  ├✔ POSITIVE
  └✘ {1}(⟨G⟩(min[V30] ≥ 1.0114283593133552e6))
   ├✔ NEGATIVE
   └✘ POSITIVE

X = scalarlogiset(X_df)
println(X)

# Accessibles on multirelation frames
# Get the structure ("frame") of the first instance. It is a "dimensional" frame, of all interval from 1 to 51.
fr = SoleLogics.frame(X, 1)

# Enumerate all worlds
collect(allworlds(fr))

using SoleLogics: Interval
# Enumerate the intervals that are "Later" than [1,10]
accessibles(fr, Interval(1,10), IA_L) |> collect

# Remember that features are computed on each world
# Let's compute the minimum of the first variable on an arbitrary interval, that is, "X[Hand tip l]".
feature = UnivariateMin(1)
Sole.featvalue(feature, X, 1, Interval(10,30))

# Remember that atoms are *scalar conditions on features*
# Let's check one on an interval of the first instance
p = Atom(ScalarCondition(feature, >, -0.5))
check(p, X, 1, Interval(10,30))

# I can check any formula
p = Atom(ScalarCondition(UnivariateMin(1), >, -0.5))
q = Atom(ScalarCondition(UnivariateMin(2), <=, 10))
φ = p ∨ q
check(φ, X, 1, Interval(10,30))

# Generate a random HS formula with scalar conditions on features, and check it on the dataset.
features = [UnivariateMin(i_variable) for i_variable in 1:ncol(X_df)]
alpha = [Atom(ScalarCondition(feat, >, thresh)) for feat in features for thresh in 0:0.1:1]

HS_connectives = SoleLogics.diamondsandboxes(SoleLogics.IARelations)
propo_connectives = SoleLogics.BASE_PROPOSITIONAL_CONNECTIVES

println("Propositional connectives: $(join(syntaxstring.(propo_connectives), ", "))")
println("HS connectives: $(join(syntaxstring.(HS_connectives), ", "))")

propo_weights = fill(1/length(propo_connectives), length(propo_connectives))
HS_weights = fill(1/length(HS_connectives), length(HS_connectives))

connectives = vcat(propo_connectives, IA_connectives)

opweights = vcat(propo_weights, HS_weights)
    
treeheight = 3
φ2 = randformula(Random.MersenneTwister(30), treeheight, alpha, connectives; opweights = opweights)
println()
println("Random formula:")
println(syntaxstring(φ2))
        
check(φ2, X, 1, Interval(10,30))

# Let's check a formula on all the instances
check_mask = check(φ2, X, Interval(10,30))

# It holds on part of the instances
sum(check_mask)

# Let's ask whether the formula holds *all* intervals, instead of checking it on [1,2]
println("Applying the universal global operator: ", SoleLogics.globalbox)
println()

universal_φ = globalbox(φ2)
println("Formula: ", syntaxstring(universal_φ))
check_mask = check(universal_φ, X)

# It holds on no instance... Too restrictive!
sum(check_mask)

# Let's ask whether there exists any interval where the formula holds
println("Applying the existential global operator: ", SoleLogics.globaldiamond)
println()

existential_φ = globaldiamond(φ2)
println("Formula: ", syntaxstring(existential_φ))
check_mask = check(existential_φ, X)

# It holds on more instances
sum(check_mask)

# Question: does it lead to a good rule?

println(syntaxstring(existential_φ))

println()
println(SoleLogics.experimentals.formula2natlang(existential_φ))

neg_existential_φ = normalize(¬ existential_φ;
    profile = :readability,
    remove_implications = true,
    allow_atom_flipping = true
)

syntaxstring(neg_existential_φ; remove_redundant_parentheses = false)

(SoleLogics.precedence(∧), SoleLogics.precedence(∨))

countmap(y)

println(existential_φ)
countmap(y[check_mask])

println(neg_existential_φ)
countmap(y[(!).(check_mask)])

branch = Branch(neg_existential_φ, "I have command", "Spread wings")

y_preds = SoleModels.apply(branch, X)
println("Accuracy of this branch: $(sum(y .== y_preds)/length(y))")
println("Random chanc e: $(60/length(y))")

println(X)

# Randomly split the data: 20% training, 80% testing
N = nrow(X_df)
perm = randperm(Random.MersenneTwister(1), N)
train_idxs, test_idxs = perm[1:round(Int, N*.2)], perm[round(Int, N*.2)+1:end];
println("Using $(length(train_idxs)) instances for training")
println("Using $(length(test_idxs)) instances for testing")

using ModalDecisionTrees

# Bind a machine learning algorithm to logiset & labels
mach = machine(ModalDecisionTree(; relations = :IA7, features = [minimum]), X_df, y)

# Train!
@time fit!(mach; rows=train_idxs);

# Compute accuracy
yhat = predict_mode(mach; rows=test_idxs)
MLJ.accuracy(yhat, y[test_idxs])

# Show the restricted MDT learnt
printmodel(report(mach).rawmodel_full; hidemodality = true)

# Show its *pure* version
printmodel(report(mach).solemodel_full; show_metrics = true, hidemodality = true)

simplified_restricted_tree = ModalDecisionTrees.prune(report(mach).rawmodel_full; simplify = true)
printmodel(simplified_restricted_tree)

println()
println("# Leaves: ", nleaves(simplified_restricted_tree))

solemodel = ModalDecisionTrees.translate(simplified_restricted_tree)

# Print leaf rules + their training performances
ruleset = listrules(solemodel)
printmodel.(ruleset; show_metrics = true, threshold_digits = 2, variable_names_map = [names(X_df)],parenthesize_atoms = false, hidemodality = true);

last_rule = ruleset[end]
last_antd = antecedent(last_rule)

println("First formula, translated:")
println(SoleLogics.experimentals.formula2natlang(last_antd; threshold_digits = 2, use_feature_abbreviations = true))
    

for (i_rule, rule) in enumerate(ruleset)
    println()
    println("[$i_rule]")
    antd = antecedent(rule)
    println(SoleLogics.experimentals.formula2natlang(antd; threshold_digits = 2, use_feature_abbreviations = true), "\n");
end

# Print rules + their *test* performances

# Sprinkle the model with the test instances!
predictions, tree_test = report(mach).sprinkle(X_df[test_idxs,:], y[test_idxs]; simplify = true);

# Extract ruleset and print its metrics
ruleset_test = listrules(tree_test);

# printmodel.(ruleset_test; show_metrics = true, threshold_digits = 2, variable_names_map = [names(X_df)], parenthesize_atoms = true, hidemodality = true);
printmodel.(ruleset_test; show_metrics = true, threshold_digits = 2, parenthesize_atoms = false, hidemodality = true);

println("IF\n\t", SoleLogics.experimentals.formula2natlang(antecedent(ruleset_test[4])))
println("THEN\n\t", consequent(ruleset_test[4]))

# Obtain class rules & show their *test* metrics
condensed_ruleset_test = joinrules(ruleset_test)
printmodel.(condensed_ruleset_test; show_metrics = true, threshold_digits = 2, parenthesize_atoms = false, hidemodality = true);

Row	cough_F1	cough_F2	cough_F3	cough_F4	cough_F5	cough_F6	cough_F7	cough_F8	cough_F9	cough_F10	cough_F11	cough_F12	cough_F13	cough_F14	cough_F15	cough_F16	cough_F17	cough_F18	cough_F19	cough_F20	cough_F21	cough_F22	cough_F23	cough_F24	cough_F25	cough_F26	cough_F27	cough_F28	cough_F29	cough_F30
	SubArray…	SubArray…	SubArray…	SubArray…	SubArray…	SubArray…	SubArray…	SubArray…	SubArray…	SubArray…	SubArray…	SubArray…	SubArray…	SubArray…	SubArray…	SubArray…	SubArray…	SubArray…	SubArray…	SubArray…	SubArray…	SubArray…	SubArray…	SubArray…	SubArray…	SubArray…	SubArray…	SubArray…	SubArray…	SubArray…
1	[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[0.0658202, 0.0389503, 0.505408, 0.659653, 0.479911, 0.198311, 0.038341, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[0.0130705, 0.00773471, 0.100364, 0.130993, 0.0953002, 0.0393805, 0.00761372, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[0.00454093, 0.00181044, 0.00463918, 0.00812784, 0.0114677, 0.00279443, 0.00452539, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[0.44294, 0.176597, 0.452524, 0.792822, 1.11861, 0.27258, 0.441425, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[1.35527, 0.337327, 2.38008, 3.41855, 1.53125, 2.44285, 1.16185, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[1.45399, 0.207212, 0.596481, 0.796232, 2.05163, 0.889234, 0.912046, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[8.8225, 4.00392, 3.61966, 3.75479, 5.86163, 7.57988, 21.0325, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[68.6091, 57.6602, 42.1623, 22.0087, 44.1134, 183.205, 231.173, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[324.805, 251.949, 146.992, 89.8209, 1076.5, 1405.0, 521.713, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[529.788, 319.674, 3170.16, 8754.94, 12131.5, 2635.83, 160.221, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[135.031, 65.4094, 31734.0, 45007.7, 9341.06, 356.08, 348.502, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[277.601, 149.644, 6192.97, 6410.55, 526.879, 1016.61, 3058.69, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[2463.94, 801.421, 218.482, 405.665, 5262.74, 5401.63, 1438.67, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[20281.1, 1322.6, 18382.4, 36321.8, 27790.6, 2262.41, 250.124, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[52706.7, 3379.9, 76705.7, 70649.4, 15600.4, 8659.85, 214.708, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[12469.4, 2027.9, 9573.34, 62751.8, 87979.5, 10106.8, 1175.91, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[5234.93, 3442.71, 27537.3, 49157.6, 34354.6, 7615.88, 590.886, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[10326.8, 8742.0, 29835.0, 65663.0, 37215.1, 9935.33, 1336.86, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[12937.2, 12652.3, 20925.7, 25481.2, 14228.1, 6209.21, 1830.89, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[15842.3, 11817.6, 20551.3, 66505.5, 53052.5, 15314.6, 4047.24, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[19539.8, 11725.6, 14110.9, 22741.0, 17571.2, 7866.31, 1784.98, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[7.47217e5, 2.85017e5, 1.91229e5, 161490.0, 74628.4, 5705.67, 230.46, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[2.82774e6, 1.12981e6, 6.8965e5, 5.55173e5, 2.28055e5, 11742.2, 526.767, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[1.8358e6, 8.47808e5, 3.64742e5, 3.14704e5, 1.72522e5, 31809.9, 4437.46, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[7.77569e5, 2.90888e5, 286646.0, 3.78292e5, 3.44293e5, 1.54095e5, 40524.1, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[4.94437e6, 1.92515e6, 9.28236e5, 2.59508e5, 68230.9, 40644.9, 7026.93, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[3.34059e6, 1.58093e6, 8.05443e5, 5.38573e5, 2.96582e5, 84622.9, 7822.09, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
2	[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[1.62005, 0.648436, 0.248455, 0.272204, 0.115829, 0.701655, 0.730142, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[0.321709, 0.128766, 0.0493379, 0.0540539, 0.0230012, 0.139334, 0.144991, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[0.0714415, 0.0461039, 0.0216893, 0.0326628, 0.019149, 0.0363546, 0.0479683, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[6.96869, 4.49716, 2.11566, 3.18606, 1.86787, 3.54618, 4.67902, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[21.754, 27.4655, 19.0048, 24.0514, 5.83474, 28.537, 43.6516, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[34.8852, 51.9125, 36.1764, 19.2253, 2.13287, 35.007, 37.3961, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[234.05, 267.416, 167.449, 62.1158, 12.2897, 151.209, 164.396, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[1454.56, 852.282, 446.942, 150.591, 88.5331, 443.756, 528.156, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[5123.56, 1131.67, 374.306, 911.971, 754.804, 356.213, 517.174, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[20084.3, 3480.32, 672.117, 10392.1, 9281.27, 898.532, 1178.98, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[15334.0, 5013.77, 1319.61, 5811.28, 5734.28, 2550.84, 2744.52, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[12234.9, 6993.57, 1761.05, 651.683, 2808.25, 2890.68, 3339.57, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[16674.7, 9460.58, 2878.9, 4119.18, 14762.8, 2806.11, 2028.18, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[56245.3, 21624.0, 6520.64, 41263.1, 63368.1, 7808.71, 4931.2, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[78468.2, 26281.8, 10982.8, 5368.62, 20602.4, 14850.2, 14849.9, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[77471.7, 95735.0, 16905.6, 8342.2, 14210.0, 9115.79, 12719.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[67572.1, 1.03941e5, 40420.4, 10467.9, 8491.01, 4898.5, 5365.45, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[1.55524e5, 2.6226e5, 1.04882e5, 18739.8, 12746.1, 7503.62, 8759.01, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[4.68154e5, 9.07885e5, 4.04412e5, 335687.0, 89441.4, 16392.0, 2126.91, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[6.92221e5, 6.03052e5, 1.1908e5, 58759.4, 31267.6, 5223.93, 1736.83, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[8.52602e5, 8.20567e5, 2.25646e5, 37287.0, 6070.59, 1029.12, 662.558, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[3.00165e6, 3.72761e6, 8.67491e5, 1.97835e5, 62275.3, 8543.62, 461.849, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[4.58505e6, 3.62319e6, 738156.0, 1.61754e5, 79843.7, 10647.7, 336.603, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[9.08381e6, 5.79745e6, 6.97654e5, 2.33825e5, 64936.9, 6078.8, 144.706, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[1.14781e7, 1.13822e7, 3.18543e6, 5.57376e5, 80210.4, 2854.2, 188.969, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[1.06059e7, 7.44956e6, 1.48442e6, 5.76729e5, 2.46594e5, 21856.1, 293.806, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[4.21352e6, 2.97859e6, 1.15466e6, 2.21273e5, 1.06559e5, 13745.5, 233.347, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
3	[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[2.95991, 1.13398, 0.151934, 0.122773, 0.0847807, 0.0358195, 0.023554, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[0.587777, 0.225185, 0.0301709, 0.0243801, 0.0168357, 0.00711301, 0.00467733, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[0.287868, 0.100246, 0.00532329, 0.00555218, 0.00194837, 0.00139787, 0.0025744, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[28.0798, 9.77835, 0.519255, 0.541582, 0.190052, 0.136353, 0.251118, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[87.0256, 27.2477, 1.62891, 3.42497, 5.70717, 1.38103, 0.747201, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[26.205, 12.3015, 0.710897, 3.31912, 11.2409, 1.91642, 0.622233, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[104.305, 28.5733, 1.47273, 7.72693, 33.6984, 6.1959, 2.24012, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[787.131, 35.6819, 2.14433, 6.87128, 40.5095, 11.3619, 6.56239, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[2810.15, 26.3572, 2.60753, 5.33105, 13.035, 15.2908, 14.1083, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[1441.28, 70.0247, 4.71634, 55.6059, 144.928, 64.3011, 28.6293, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[310.68, 134.696, 6.17189, 77.5938, 548.601, 169.862, 26.9415, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[4312.45, 430.115, 36.0941, 59.7097, 699.837, 515.62, 121.397, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[9462.69, 1239.38, 1889.39, 1715.57, 4571.37, 3039.89, 521.224, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[12799.2, 5572.2, 4783.39, 2921.7, 3869.14, 2904.15, 287.716, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[48185.1, 22933.0, 8947.61, 1410.66, 5173.81, 3749.97, 285.372, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[39728.7, 34053.4, 18530.0, 3214.0, 8251.46, 5393.83, 295.867, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[18179.9, 17902.9, 13901.1, 3031.17, 4455.63, 2813.3, 276.484, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[23039.8, 16723.5, 10761.6, 2520.38, 6145.44, 4647.94, 1075.16, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[23699.7, 24883.3, 25941.0, 19031.7, 63086.4, 65503.3, 6569.06, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[22270.5, 36373.6, 43627.6, 20155.9, 51197.1, 41757.6, 2081.54, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[7335.9, 7787.29, 6425.02, 1077.41, 5805.58, 5090.74, 565.701, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[12454.8, 61374.0, 37532.5, 3169.38, 17814.9, 17397.2, 1254.45, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[85598.0, 1.32e5, 77841.8, 10685.4, 62715.7, 62057.9, 3981.02, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[3.3401e5, 4.85673e5, 2.34642e5, 37125.9, 67977.1, 49896.6, 1859.31, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[3.76757e5, 3.1447e5, 2.0514e5, 47156.2, 29565.1, 12876.3, 314.342, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[1.85178e5, 41202.4, 25935.4, 9675.14, 12257.0, 7197.6, 409.98, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[1.87532e5, 1.68534e5, 1.27423e5, 25359.9, 10386.6, 3348.69, 403.783, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
4	[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[3.66094, 0.709991, 0.342895, 0.265306, 0.0468018, 0.0975574, 0.0396136, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[0.726986, 0.140989, 0.0680918, 0.0526841, 0.00929385, 0.0193729, 0.00786644, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[0.596604, 0.143737, 0.0243013, 0.017446, 0.00756199, 0.00591798, 0.000982618, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[58.1952, 14.0207, 2.37045, 1.70176, 0.737627, 0.577263, 0.0958485, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[147.965, 47.2162, 6.6428, 5.11241, 2.9279, 8.16827, 2.79756, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[40.9492, 17.5678, 2.81489, 2.28017, 1.23771, 13.8401, 6.63218, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[87.6788, 41.5942, 7.84765, 8.43343, 3.92466, 37.327, 19.8962, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[358.114, 84.7886, 19.3406, 23.5386, 8.72719, 40.1797, 25.6276, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[1598.58, 206.555, 34.0308, 26.9274, 7.90228, 10.7571, 13.6902, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[1603.69, 353.238, 47.394, 21.8011, 5.74293, 109.841, 102.113, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[1758.73, 496.494, 52.2081, 15.8906, 4.78958, 681.119, 554.578, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[3664.62, 812.224, 50.6326, 67.4201, 43.7704, 334.839, 351.277, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[7484.34, 2693.82, 2067.11, 2069.21, 194.07, 590.997, 640.186, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[11556.3, 29918.0, 21582.0, 2577.26, 753.553, 2049.77, 2298.09, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[30503.8, 28336.8, 13649.4, 5493.29, 3762.58, 4907.5, 5493.42, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[25393.4, 22818.6, 7584.81, 9041.14, 3711.21, 1887.35, 2093.86, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[34420.9, 1.18214e5, 94485.0, 19588.8, 3140.11, 3641.38, 2891.79, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[40911.6, 91877.1, 1.08737e5, 43639.5, 6988.58, 6171.03, 8560.34, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[64319.5, 93008.7, 71986.6, 31100.6, 9277.21, 28268.3, 41510.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[36343.9, 45441.8, 50437.0, 24456.9, 7994.68, 11499.8, 14622.1, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[31704.3, 10317.3, 7245.11, 4462.03, 1025.48, 1940.55, 3478.32, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[74359.6, 69897.9, 64846.6, 34381.0, 3091.71, 13139.2, 29797.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[2.78057e5, 3.32722e5, 1.90903e5, 87703.6, 15395.8, 64121.8, 1.12202e5, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[7.02111e5, 8.22605e5, 6.52635e5, 2.68491e5, 26049.9, 45940.9, 64670.5, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[2.72789e5, 2.95965e5, 2.2296e5, 92396.7, 17608.2, 13774.1, 9150.45, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[364318.0, 2.32307e5, 93574.5, 16249.4, 3713.91, 1830.37, 1220.76, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[4.8397e5, 4.85444e5, 4.74261e5, 2.31186e5, 37388.0, 6486.39, 2700.48, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
5	[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[1.26265, 0.966647, 0.281845, 0.14006, 0.0330181, 0.0189545, 0.0202496, 0.00427777, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[0.250736, 0.191956, 0.0559685, 0.027813, 0.00655671, 0.00376397, 0.00402115, 0.000849476, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[0.128993, 0.0764966, 0.0233883, 0.0117256, 0.00282783, 0.00201767, 0.00842082, 0.00644938, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[12.5825, 7.46179, 2.28138, 1.14377, 0.275838, 0.196811, 0.821401, 0.629098, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[39.6493, 29.8172, 6.77437, 4.26332, 1.11107, 0.975898, 4.93697, 3.74938, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[19.4749, 16.3886, 4.12483, 2.67541, 0.464519, 0.445975, 1.11086, 0.813915, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[51.152, 42.9527, 10.3997, 6.5627, 0.949258, 0.924119, 2.19942, 2.05453, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[111.898, 63.613, 17.1275, 8.80279, 1.06797, 0.925928, 4.48152, 7.07233, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[569.985, 108.755, 22.6375, 7.41687, 1.46129, 0.643923, 12.8904, 17.9929, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[3222.3, 288.182, 23.4452, 12.9812, 3.44978, 1.28711, 10.3539, 15.2015, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[1335.78, 165.667, 30.1376, 19.7937, 21.5971, 6.51968, 13.4725, 16.0345, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[777.711, 324.876, 109.027, 41.0903, 36.397, 20.945, 14.2237, 17.5114, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[10828.7, 1668.78, 323.53, 239.753, 539.395, 350.217, 87.928, 47.3984, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[78474.6, 39717.9, 5171.09, 5031.24, 7465.97, 1017.16, 471.802, 84.2813, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[57288.9, 58937.8, 63179.9, 41833.8, 10112.4, 2065.01, 179.165, 129.782, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[23679.4, 12135.6, 4153.62, 2523.42, 3794.78, 2171.68, 95.8407, 44.9481, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[40812.2, 67968.1, 46090.7, 37924.2, 35747.9, 12381.9, 493.346, 127.652, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[47647.5, 94273.0, 123220.0, 2.08642e5, 1.44458e5, 13063.8, 708.991, 314.243, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[56176.9, 1.60908e5, 2.16831e5, 226223.0, 1.06068e5, 3124.26, 348.297, 267.814, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[17601.5, 13799.6, 8708.93, 13248.8, 15733.6, 1562.7, 172.311, 125.331, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[30268.5, 6329.52, 2926.14, 4592.41, 3606.03, 1299.82, 173.137, 109.924, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[1.4524e5, 97346.0, 77712.2, 66049.1, 32499.9, 2262.61, 255.482, 134.958, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[1.45041e5, 1.85927e5, 1.45862e5, 1.00064e5, 49656.9, 9471.9, 514.132, 164.417, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[1.28999e6, 1.78603e6, 9.06583e5, 361846.0, 1.48162e5, 21636.4, 813.163, 287.636, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[3.107e5, 4.77255e5, 3.0263e5, 251105.0, 1.34015e5, 9386.39, 958.033, 412.196, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[3.25589e5, 2.59251e5, 1.49e5, 82328.3, 33612.5, 4056.65, 366.101, 145.276, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[6.30103e5, 265040.0, 2.20978e5, 1.96787e5, 1.01931e5, 9350.2, 658.859, 225.53, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
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10	[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[3.37013, 1.19436, 0.97079, 0.600305, 0.176715, 0.0773258, 0.0182055, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[0.669236, 0.237175, 0.192778, 0.119208, 0.0350919, 0.0153553, 0.00361524, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[0.446583, 0.20182, 0.068445, 0.0334676, 0.0137318, 0.00983118, 0.00542909, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[43.5615, 19.6863, 6.6764, 3.26457, 1.33945, 0.958973, 0.529576, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[156.355, 68.3481, 19.5355, 9.7452, 19.7202, 34.0038, 4.53388, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[66.141, 21.6009, 9.70487, 3.89796, 20.7443, 34.8802, 2.44411, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[294.463, 62.8391, 28.09, 11.3367, 46.0355, 71.1349, 5.46733, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[891.481, 157.743, 62.8578, 31.4354, 38.1107, 42.9304, 11.1868, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[2352.51, 365.704, 95.5167, 49.7098, 24.0039, 48.7936, 25.1504, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[8246.09, 967.78, 155.305, 47.334, 291.603, 534.511, 67.0029, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[6024.03, 1189.12, 265.899, 56.1783, 266.784, 384.745, 171.963, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[3533.57, 1226.86, 287.814, 107.679, 76.4183, 117.665, 74.5026, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[17523.0, 7519.97, 2077.73, 284.892, 134.043, 221.827, 76.6903, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[45841.8, 30133.3, 13270.2, 5192.14, 573.511, 1364.3, 588.844, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[92531.6, 21482.3, 22825.5, 8430.95, 1080.04, 4441.88, 2314.94, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[54982.9, 29508.2, 17050.0, 1898.75, 2338.2, 4518.82, 680.442, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[71814.8, 89066.3, 25261.9, 2756.71, 944.552, 1880.57, 521.817, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[60571.1, 89317.5, 60506.8, 8202.06, 2781.65, 6776.13, 2259.45, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[70759.6, 82738.4, 46046.0, 5315.16, 4730.53, 11924.1, 3320.59, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[78538.9, 43817.4, 24255.7, 3411.18, 3133.98, 5237.83, 1218.67, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[97326.6, 22813.3, 14944.5, 3651.76, 2013.17, 3132.29, 1212.68, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[1.23273e5, 1.14776e5, 82447.6, 10985.9, 2914.84, 5551.98, 2231.11, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[3.77328e5, 2.11186e5, 132127.0, 20059.1, 6631.32, 7568.12, 2523.18, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[6.38947e5, 5.47384e5, 3.78993e5, 22361.1, 3761.69, 5458.03, 2191.26, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[6.10758e5, 3.41778e5, 1.82234e5, 18213.7, 7352.09, 8288.47, 1414.14, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[3.19789e5, 2.57071e5, 1.15207e5, 17495.7, 3363.3, 2429.97, 443.019, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[674707.0, 4.4471e5, 1.95013e5, 34913.7, 7769.58, 4178.22, 338.683, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
11	[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[2.34154, 0.925767, 0.378433, 0.123683, 0.0554886, 0.0348641, 0.0349075, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[0.464981, 0.183838, 0.0751489, 0.0245608, 0.0110189, 0.00692327, 0.0069319, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[0.388725, 0.139274, 0.0471144, 0.0156152, 0.0121435, 0.0237099, 0.0155309, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[37.9178, 13.5853, 4.59573, 1.52317, 1.18452, 2.31276, 1.51494, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[217.601, 77.2663, 10.8263, 6.32299, 13.4739, 31.2038, 9.66738, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[106.796, 32.4735, 5.26017, 2.09609, 5.57463, 12.6613, 2.43583, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[277.033, 64.1409, 11.2031, 4.36364, 8.79765, 18.0226, 4.11183, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[999.644, 81.9154, 13.4892, 5.16238, 5.41049, 12.1697, 12.3495, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[2400.27, 87.6089, 17.6806, 4.22724, 7.004, 50.1794, 36.2726, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[2639.01, 194.706, 34.3255, 10.1684, 14.6814, 84.9472, 27.6049, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[2106.86, 301.505, 53.3498, 16.7153, 15.2046, 30.8032, 11.4269, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[1423.65, 362.226, 43.7223, 20.82, 31.2697, 71.514, 48.2697, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[2722.88, 817.225, 132.639, 197.576, 143.617, 164.932, 130.755, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[25460.9, 4361.11, 4820.59, 3457.14, 356.896, 232.836, 167.751, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[51125.5, 47000.3, 17446.0, 1250.98, 207.364, 574.049, 294.856, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[80356.0, 9525.29, 1177.41, 763.166, 729.577, 643.218, 193.964, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[41449.3, 7972.57, 4625.34, 3406.24, 352.905, 396.441, 214.448, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[59798.9, 42373.6, 17077.1, 2659.07, 345.079, 1190.74, 996.585, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[1.04703e5, 91496.9, 11170.0, 3893.02, 1574.94, 2627.33, 1684.2, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[61889.9, 10436.1, 3856.79, 1454.4, 1088.22, 1116.16, 535.533, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[24234.8, 2942.63, 1284.6, 289.374, 168.726, 254.392, 177.22, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[1.12408e5, 21933.1, 9727.37, 1733.79, 437.41, 825.012, 309.154, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[1.04032e5, 49172.9, 25588.7, 7479.12, 1876.08, 1661.48, 666.537, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[784536.0, 3.04213e5, 1.13671e5, 21546.3, 4499.85, 2484.3, 1116.24, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[1.09377e6, 6.04365e5, 2.83506e5, 68889.4, 17995.0, 18668.1, 8841.47, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[1.6458e6, 6.22042e5, 2.81978e5, 65010.7, 12437.2, 8176.51, 2151.71, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[253010.0, 66991.3, 25185.6, 7200.73, 2197.7, 1289.79, 449.488, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
12	[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[3.07298, 2.57072, 0.687283, 0.322323, 0.214695, 0.0641531, 0.0246654, 0.0356907, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[0.61023, 0.510491, 0.13648, 0.0640066, 0.0426338, 0.0127395, 0.00489802, 0.00708743, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[0.204552, 0.231794, 0.051393, 0.0287529, 0.0233669, 0.00844628, 0.00442164, 0.00340762, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[19.9528, 22.6101, 5.01308, 2.80468, 2.27931, 0.823884, 0.431304, 0.332393, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[75.7704, 65.3334, 10.8787, 6.68328, 39.0807, 17.2155, 1.48772, 0.433085, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[85.5894, 46.8714, 3.23801, 2.35077, 27.7438, 12.7074, 0.313934, 0.394331, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[482.028, 127.0, 12.7559, 5.77321, 47.7432, 22.6437, 2.13238, 1.44283, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[2864.31, 239.924, 46.5537, 8.46633, 28.6383, 27.855, 14.3802, 3.16736, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[5418.38, 252.033, 49.7085, 12.1586, 78.9562, 98.4969, 29.3789, 6.39385, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[1941.13, 474.993, 39.41, 19.0993, 368.63, 219.226, 36.7584, 16.67, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[1568.86, 603.34, 40.1766, 11.4549, 110.336, 109.271, 62.0838, 27.2405, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[1490.5, 383.782, 55.6588, 16.121, 104.094, 90.6794, 63.1952, 42.7605, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[2559.54, 410.284, 122.797, 36.1423, 469.858, 333.973, 122.41, 72.8257, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[14168.3, 672.405, 99.8046, 88.484, 1503.26, 1371.22, 366.758, 104.775, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[51757.4, 1222.96, 102.75, 262.084, 3539.11, 4070.57, 499.431, 90.2152, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[22181.9, 1796.59, 196.855, 442.556, 3415.11, 1717.36, 135.926, 31.1462, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[33665.6, 16849.0, 4569.12, 1640.58, 2192.02, 1568.63, 203.892, 46.781, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[40277.0, 29092.3, 12903.3, 1254.84, 5150.63, 4957.78, 1353.79, 397.302, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[64674.9, 19832.0, 4520.22, 1743.34, 14492.1, 15797.4, 3462.22, 550.115, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[66666.6, 2453.63, 583.168, 579.314, 2588.16, 2377.38, 676.225, 167.056, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[76701.9, 2966.63, 618.305, 422.079, 1663.13, 1707.88, 745.378, 320.232, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[1.77635e5, 11485.5, 2535.62, 1375.23, 4137.03, 3490.8, 731.529, 215.071, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[3.9469e5, 30881.1, 18810.2, 5159.56, 9893.79, 8561.0, 1606.42, 419.461, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[792515.0, 59472.9, 27191.6, 6990.04, 7554.09, 8188.63, 3081.72, 1442.57, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[5.27641e5, 86196.2, 46352.5, 21304.0, 19972.1, 6194.02, 3036.64, 1981.62, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[1.331e6, 93031.1, 34402.6, 12832.2, 9110.71, 3529.93, 675.309, 230.184, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[4.92566e5, 41036.7, 25526.5, 8916.29, 11981.5, 4253.0, 453.673, 173.776, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
13	[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[1.77748, 0.796787, 0.0738923, 0.0442614, 0.0555183, 0.0385192, 0.0508226, 0.101736, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[0.352971, 0.158225, 0.0146735, 0.00878938, 0.0110248, 0.0076491, 0.0100923, 0.0202027, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[0.203237, 0.0634473, 0.00590952, 0.00739434, 0.0543729, 0.0397721, 0.0168973, 0.0131004, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[19.8246, 6.1889, 0.576438, 0.721273, 5.30375, 3.87953, 1.64823, 1.27787, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[124.634, 54.834, 2.34781, 2.41349, 40.6377, 23.3851, 3.94196, 1.50084, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[186.581, 65.7612, 2.12284, 0.663678, 11.2329, 5.3498, 0.707124, 0.730327, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[704.364, 148.441, 5.73875, 1.69698, 15.755, 11.4785, 4.47877, 3.11821, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[2321.02, 133.551, 7.94585, 2.78301, 22.3367, 44.4627, 18.6896, 7.98156, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[2514.79, 41.9798, 5.11629, 2.87657, 104.26, 128.672, 27.8394, 24.7023, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[1209.35, 111.5, 7.05355, 7.63288, 108.886, 87.4096, 54.1756, 56.6978, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[1087.4, 86.65, 5.31522, 7.86358, 46.7023, 19.3707, 18.692, 9.42548, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[1095.46, 43.8612, 4.47532, 7.27559, 90.0504, 95.3982, 46.992, 23.5355, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[5313.41, 98.9343, 5.80101, 20.4876, 538.687, 481.1, 178.959, 27.1045, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[52939.2, 182.635, 24.9805, 46.2887, 535.117, 418.2, 154.758, 13.453, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[63418.5, 317.791, 101.497, 253.755, 2012.01, 1969.08, 242.854, 13.0324, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[25400.0, 844.502, 2453.88, 2756.33, 1992.17, 926.157, 198.911, 14.63, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[43050.9, 13510.0, 8776.78, 2580.14, 1100.79, 689.201, 244.525, 21.4951, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[32548.9, 10756.9, 2708.59, 396.899, 1883.98, 2075.85, 700.733, 74.3869, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[49564.7, 1816.13, 950.232, 1144.21, 5622.47, 2581.87, 1350.99, 317.29, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[35322.3, 2213.58, 1094.95, 1597.92, 5503.05, 3409.37, 1128.94, 376.748, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[23695.3, 1530.78, 284.107, 181.767, 696.674, 723.757, 327.421, 132.597, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[1.50965e5, 6586.02, 1725.3, 1047.81, 1947.87, 1762.02, 579.249, 188.906, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[1.22333e5, 13705.6, 7470.78, 3432.98, 3172.74, 2108.98, 419.699, 87.7327, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[1.06682e6, 1.3009e5, 58963.0, 9291.6, 4988.88, 3195.24, 708.604, 118.306, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[1.17282e6, 1.21511e5, 68782.6, 19980.3, 11376.4, 6650.9, 806.036, 138.297, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[5.32616e5, 38504.2, 18766.0, 7171.64, 6999.12, 3885.44, 474.652, 143.486, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[1.15976e6, 66152.8, 21711.5, 6822.59, 10863.9, 4850.65, 571.616, 216.577, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮
186	[0.0174438, 0.00310092, 0.00849625, 0.026699, 0.0209502, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[0.0132189, 0.00241784, 0.0035302, 0.00828464, 0.00804756, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[0.00534833, 0.000978251, 0.00142831, 0.00335194, 0.00325602, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[0.00761603, 0.00336311, 0.00303914, 0.0176728, 0.00857069, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[0.00685904, 0.0030301, 0.00274326, 0.0159144, 0.007736, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[0.063674, 0.0279074, 0.0298889, 0.130429, 0.0989523, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[0.0711588, 0.0281567, 0.0296447, 0.0901159, 0.0834384, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[0.191892, 0.134706, 0.0402976, 0.270762, 0.343598, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[0.924332, 0.977029, 0.841115, 1.50237, 2.78743, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[22.6832, 9.07198, 17.4938, 19.778, 27.2206, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[133.13, 22.4991, 145.724, 216.318, 137.163, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[251.369, 12.5201, 212.579, 1615.37, 687.172, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[454.676, 27.8568, 41.6125, 7433.74, 4648.45, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[292.397, 115.87, 158.062, 1159.76, 966.212, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[766.985, 282.466, 978.475, 1031.66, 682.515, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[5574.79, 3934.18, 5856.77, 13017.9, 3727.62, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[10504.4, 1131.58, 1335.97, 9885.21, 5373.02, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[15786.8, 2770.87, 3241.64, 12780.9, 2412.14, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[12599.6, 5966.12, 4462.94, 10123.4, 2109.77, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[11800.9, 9975.36, 9992.27, 9421.64, 4391.57, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[41242.4, 25335.9, 16439.9, 43828.2, 20822.6, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[1.64176e5, 90518.6, 10890.7, 11985.6, 3423.28, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[2.33637e5, 74169.5, 19976.8, 13575.9, 2001.45, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[1.88033e6, 7.10186e5, 1.77592e5, 1.35604e5, 25446.5, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[3.03456e6, 1.19725e6, 1.54486e5, 1.41498e5, 26203.5, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[3.72399e6, 1.27537e6, 1.5455e5, 1.32944e5, 23047.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[2.99741e6, 1.12252e6, 1.88817e5, 2.71814e5, 52557.7, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[6.89062e5, 2.29374e5, 1.43654e5, 1.13484e5, 16261.7, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
187	[0.0291811, 0.0169011, 0.0162059, 0.0177519, 0.121101, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[0.0421715, 0.00966519, 0.00904388, 0.0169365, 0.0497429, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[0.0170625, 0.00391051, 0.00365913, 0.00685247, 0.0201259, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[0.181893, 0.0464096, 0.00634378, 0.0110066, 0.0522485, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[0.164498, 0.04206, 0.00570707, 0.00988836, 0.0481563, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[2.67007, 0.829326, 0.0319864, 0.0367413, 4.99969, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[5.11151, 0.850098, 0.0569604, 0.1121, 30.7308, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[16.5958, 0.855482, 0.156334, 0.519324, 129.415, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[36.268, 0.606582, 0.304205, 0.756828, 135.214, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[71.5685, 2.29434, 1.18257, 0.512215, 28.0198, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[81.8014, 32.9256, 18.5455, 3.25372, 174.747, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[134.567, 365.304, 250.695, 16.126, 530.572, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[182.355, 349.712, 201.699, 37.9573, 119.204, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[1293.56, 735.043, 1669.64, 2641.95, 459.472, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[12848.8, 4812.97, 12542.7, 11533.4, 462.524, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[58893.1, 24212.2, 6446.18, 1336.32, 297.868, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[8009.08, 5459.35, 1346.01, 106.845, 37.4606, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[4722.67, 3870.87, 1449.97, 470.779, 69.0678, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[29559.3, 1.07566e5, 1.09275e5, 7262.55, 4893.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[2.41214e5, 3.71867e5, 1.55983e5, 22517.7, 3425.55, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[1.55783e5, 109252.0, 33611.9, 5995.83, 951.081, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[90907.3, 95050.1, 31891.2, 6297.47, 1049.79, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[40828.2, 28222.7, 17116.0, 2650.0, 502.319, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[3.7979e5, 1.98252e5, 29279.1, 2475.27, 146.264, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[1.98778e5, 74616.2, 14847.6, 1496.66, 72.7096, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[19935.9, 14455.1, 6697.27, 1191.26, 62.9539, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[18396.3, 3995.76, 473.269, 43.6156, 92.2445, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[14823.7, 6247.5, 867.695, 92.6666, 145.434, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
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197	[0.115361, 0.0464483, 0.0123202, 0.0632743, 0.11675, 0.0424258, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[0.206398, 0.0766185, 0.0210895, 0.0674126, 0.107959, 0.0308161, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[0.0835083, 0.0309996, 0.00853277, 0.027275, 0.0436798, 0.0124681, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[0.234684, 0.0745736, 0.0204655, 0.0366456, 0.0704176, 0.0359, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[0.211086, 0.0670677, 0.0184346, 0.0328961, 0.0632762, 0.0323052, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[1.12866, 0.355707, 0.136239, 0.111711, 0.28501, 0.232529, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[0.674157, 0.514101, 0.252998, 0.168851, 0.338446, 0.301313, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[0.546261, 1.26355, 0.798744, 0.419339, 1.39225, 1.44767, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[1.93425, 1.43249, 1.46607, 1.12628, 3.72256, 16.325, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[34.3193, 1.94558, 1.86993, 11.4348, 141.679, 273.495, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[746.442, 10.086, 2.40589, 232.759, 1884.17, 1262.68, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[3768.57, 46.7528, 1.98855, 6176.14, 9678.99, 692.608, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[957.84, 18.6519, 3.23436, 7440.54, 7997.95, 537.632, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[73.2896, 8.6314, 3.62732, 178.192, 1170.35, 3213.71, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[406.588, 3.68579, 1.51439, 24.9124, 4331.24, 3371.67, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[272.894, 6.17965, 1.70592, 170.465, 942.132, 272.696, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[881.39, 52.3759, 47.8773, 161.74, 3672.64, 1841.64, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[9634.62, 3250.83, 1437.82, 15526.1, 29398.7, 6510.2, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[7891.22, 3483.18, 338.92, 4682.33, 15818.6, 6142.17, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[3080.29, 1461.23, 893.893, 2698.82, 5878.18, 1772.11, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[61562.9, 42404.1, 19231.4, 13520.2, 21887.5, 4165.85, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[60104.9, 14105.5, 3402.71, 7270.35, 9808.96, 1492.81, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[93530.2, 1429.72, 646.662, 4840.68, 6426.18, 1497.02, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[3.37752e5, 3209.86, 476.808, 750.046, 1060.53, 242.128, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[4.07255e5, 5381.54, 631.243, 1725.49, 2653.75, 442.006, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[1.22697e5, 1595.19, 344.311, 1009.06, 1046.03, 121.785, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[3.67178e5, 4340.25, 438.249, 362.808, 339.1, 39.2968, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	[18475.8, 1058.32, 527.914, 1346.66, 2348.97, 732.023, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]

NATOPS: Interpretable gesture recognition¶

Exercise¶

Modal Symbolic Learning: Day 2¶

NATOPS: Interpretable gesture recognition¶

Exercise¶